阅读说明：本技术 一种基于AlexNet网络模型实现医学影像辅助诊断的方法、装置、设备及存储介质 (Method, device, equipment and storage medium for realizing medical image auxiliary diagnosis based on AlexNet network model ) 是由 李晓华 于 2019-11-26 设计创作，主要内容包括：本发明涉及医疗设备技术领域,公开了一种基于AlexNet网络模型实现医学影像辅助诊断的方法、装置、设备及存储介质。通过本发明创造,提供了一种利用人工智能中最新深度学习技术实现医学影像辅助诊断的新方法,即在具有很好泛化能力的AlexNet网络模型中,先用卷积神经网络提取各种复杂的特征,然后利用全连接网络对特征进行综合的判断,进而使模型可以涵盖各种复杂的情况,可利于在医学影像辅助诊断的全过程中,实现端到端的自动化诊断,效率和准确率都极高。此外,还可以对新增部位或影像进行迁移学习,使模型升级变得简单易操作,而且能保证对原有知识的最大兼容性,便于实际应用和推广。(The invention relates to the technical field of medical equipment, and discloses a method, a device, equipment and a storage medium for realizing medical image auxiliary diagnosis based on an AlexNet network model. The invention provides a new method for realizing medical image auxiliary diagnosis by using the latest deep learning technology in artificial intelligence, namely, in an AlexNet network model with good generalization capability, a convolutional neural network is used for extracting various complex characteristics, and then a full-connection network is used for comprehensively judging the characteristics, so that the model can cover various complex conditions, the method is favorable for realizing end-to-end automatic diagnosis in the whole process of medical image auxiliary diagnosis, and the efficiency and the accuracy are extremely high. In addition, newly added parts or images can be migrated and learned, so that model upgrading becomes simple and easy to operate, the maximum compatibility of original knowledge can be ensured, and the method is convenient for practical application and popularization.)

1. A method for realizing medical image auxiliary diagnosis based on an AlexNet network model is characterized by comprising the following steps:

s101, obtaining a plurality of sample medical images and diagnosis labels corresponding to the sample medical images, wherein the diagnosis labels are positive examples or negative examples;

s102, carrying out image preprocessing on each sample medical image to obtain a corresponding standard sample medical image which is square, consistent in size and normalized, and carrying out digital coding on a corresponding diagnosis label in the following form; if positive, it is marked as 1, and if negative, it is marked as 0;

s103, extracting 2 from the training sample data setⁿAnd then, introducing the standard sample medical images into an AlexNet network model for forward propagation to obtain a first prediction probability corresponding to each standard sample medical image, wherein the training sample data set comprises not less than 2ⁿN is a natural number between 4 and 8, and the first prediction probability comprises a probability of identifying the image as a positive example and a probability of identifying the image as a negative example;

the AlexNet network model sequentially comprises a first convolution layer, a first batch of normalization layers, a first maximum pooling layer, a second convolution layer, a second batch of normalization layers, a second maximum pooling layer, a third convolution layer, a fourth convolution layer, a fifth convolution layer, the flattening layer, a first full-connection layer, a first discarding layer, a second full-connection layer, a second discarding layer and a third full-connection layer along the forward propagation direction, wherein the convolution kernel of the first convolution layer is (11, 11), the step length is (4, 4), the number of output channels is 96, the activation function is a relu activation function, and the padding type is valid; the first normalization layer is used for accelerating network convergence, preventing overfitting and improving generalization capability; the pooling core of the first maximum pooling layer is (3, 3), the step length is (2, 2), and the padding type is valid; the convolution kernel of the second convolution layer is (5, 5), the step length is (1, 1), the number of output channels is 256, the activation function is a relu activation function, and the padding type is same; the second batch of normalization layers are also used for accelerating network convergence, preventing overfitting and improving generalization capability; the pooling core of the second maximum pooling layer is (3, 3), the step length is (2, 2), and the padding type is valid; the convolution kernel of the third convolution layer is (3, 3), the step length is (1, 1), the number of output channels is 384, the activation function is a relu activation function, and the padding type is same; the convolution kernel of the fourth convolution layer is (3, 3), the step length is (1, 1), the number of output channels is 256, the activation function is a relu activation function, and the padding type is same; the convolution kernel of the fifth convolution layer is (3, 3), the step length is (1, 1), the number of output channels is 384, the activation function is a relu activation function, and the padding type is same; the flattening layer is used for spreading the characteristic diagram into a line, so that a first full-connection layer is connected to the rear side of the flattening layer conveniently; the number of the neurons of the first full-junction layer is 1024, and the activation function is a relu activation function; the first discarding layer is used for randomly inactivating half of the neurons, so that overfitting is avoided, and generalization capability is improved; the number of neurons of the second fully-connected layer is half of the number of neurons of the first fully-connected layer, and the activation function is a relu activation function; the second discarding layer is used for randomly inactivating half of the neurons, so that overfitting is avoided, and generalization capability is improved; the number of neurons of the third full junction layer is 2, and the activation function is a softmax activation function, wherein the softmax activation function is used for identifying the probability that the image is a positive example and the probability that the image is a negative example;

s104, calculating the average loss value loss of the training according to the following formula:

wherein i is 1 to 2ⁿNatural number between, y_iThe diagnostic label code value of the ith standard sample medical image,

s105, reversely propagating the average loss value loss layer by layer through a gradient descent method, and updating model parameters;

s106, returning to execute the steps S103-S105 until the average loss value loss is reduced to a preset threshold value, and finishing model training;

s107, acquiring a medical image to be diagnosed;

s108, image preprocessing is carried out on the medical image to be diagnosed, and a standard medical image to be diagnosed which is square and has the size consistent with that of the standard sample medical image and the normalization mode consistent with that of the standard sample medical image is obtained;

s109, inputting the standard medical image to be diagnosed into an AlexNet network model which is trained to obtain a second prediction probability corresponding to the standard medical image to be diagnosed, wherein the second prediction probability comprises a probability of identifying an image as a positive example and a probability of identifying an image as a negative example;

s110, obtaining a diagnosis result according to the second prediction probability: and if the probability of identifying the positive example of the image in the second prediction probability is not less than the segmentation threshold of the positive example and the negative example, judging that the diagnosis result is positive example and/or positive, otherwise, judging that the diagnosis result is negative example and/or negative.

2. The method according to claim 1, wherein in step S102, the square sample medical image is obtained by image preprocessing in the following manner:

when the original shape of the sample medical image is a non-square rectangle, pixel points with pixel values of 0 or pixel mean values are symmetrically filled at two sides in the width direction, so that the final image width is equal to the image length, wherein the pixel mean value is the pixel mean value of all the pixel points in the original medical image.

3. The method according to claim 2, wherein after the square sample medical image is obtained in step S102, the sample medical image with the same size is obtained by image preprocessing as follows:

the sample medical image is changed into the size of 2 by interpolation or pressure sampling^k*2^kWherein k is a natural number between 7 and 12.

4. The method according to claim 3, wherein after the sample medical images with consistent sizes are obtained in step S102, the normalized sample medical images are obtained by image preprocessing in the following manner:

aiming at each pixel point on the sample medical image, obtaining a normalized pixel value P according to the following formula_New：

In the formula, P_OldThe value is the pixel value before normalization, mu is the pixel average value of all pixel points before normalization, and sigma is the pixel value standard deviation of all pixel points before normalization.

5. The method according to claim 1, wherein before the step S103, the training sample data set is subjected to data enhancement in any one or any combination of the following manners (a) to (D):

(A) randomly turning the standard sample medical image up and down and/or left and right, and then adding the obtained image as a new sample into the training sample data set;

(B) carrying out random angular rotation processing on the standard sample medical image, and then adding the obtained image serving as a new sample into the training sample data set;

(C) randomly adding Gaussian noise to the medical image of the standard sample, and then adding the obtained image serving as a new sample to the training sample data set;

(D) and randomly cutting the image of the standard sample medical image, and then adding the obtained image as a new sample into the training sample data set, wherein the image cutting part accounts for no more than 5% of the whole image.

6. A device for realizing medical image auxiliary diagnosis based on an AlexNet network model is characterized by comprising a sample acquisition module, a sample preprocessing module, a model iteration training module, an image acquisition module, an image preprocessing module, an image recognition module and an image diagnosis module, wherein the model iteration training module comprises a model training submodule, a loss calculation submodule, a parameter updating submodule and an iteration control submodule;

the sample acquisition module is used for acquiring a plurality of sample medical images and diagnosis labels corresponding to the sample medical images, wherein the diagnosis labels are positive examples or negative examples;

the sample preprocessing module is in communication connection with the sample acquisition module and is used for preprocessing images of all sample medical images to obtain corresponding standard sample medical images which are square, consistent in size and normalized, and simultaneously digitally encoding corresponding diagnosis labels in the following forms; if positive, it is marked as 1, and if negative, it is marked as 0;

the model training submodule is in communication connection with the sample preprocessing module and is used for extracting 2 from a training sample data setⁿAnd then, introducing the standard sample medical images into an AlexNet network model for forward propagation to obtain a first prediction probability corresponding to each standard sample medical image, wherein the training sample data set comprises not less than 2ⁿN is a natural number between 4 and 8, and the first prediction probability comprises a probability of identifying the image as a positive example and a probability of identifying the image as a negative example;

the AlexNet network model sequentially comprises a first convolution layer, a first batch of normalization layers, a first maximum pooling layer, a second convolution layer, a second batch of normalization layers, a second maximum pooling layer, a third convolution layer, a fourth convolution layer, a fifth convolution layer, the flattening layer, a first full-connection layer, a first discarding layer, a second full-connection layer, a second discarding layer and a third full-connection layer along the forward propagation direction, wherein the convolution kernel of the first convolution layer is (11, 11), the step length is (4, 4), the number of output channels is 96, the activation function is a relu activation function, and the padding type is valid; the first normalization layer is used for accelerating network convergence, preventing overfitting and improving generalization capability; the pooling core of the first maximum pooling layer is (3, 3), the step length is (2, 2), and the padding type is valid; the convolution kernel of the second convolution layer is (5, 5), the step length is (1, 1), the number of output channels is 256, the activation function is a relu activation function, and the padding type is same; the second batch of normalization layers are also used for accelerating network convergence, preventing overfitting and improving generalization capability; the pooling core of the second maximum pooling layer is (3, 3), the step length is (2, 2), and the padding type is valid; the convolution kernel of the third convolution layer is (3, 3), the step length is (1, 1), the number of output channels is 384, the activation function is a relu activation function, and the padding type is same; the convolution kernel of the fourth convolution layer is (3, 3), the step length is (1, 1), the number of output channels is 256, the activation function is a relu activation function, and the padding type is same; the convolution kernel of the fifth convolution layer is (3, 3), the step length is (1, 1), the number of output channels is 384, the activation function is a relu activation function, and the padding type is same; the flattening layer is used for spreading the characteristic diagram into a line, so that a first full-connection layer is connected to the rear side of the flattening layer conveniently; the number of the neurons of the first full-junction layer is 1024, and the activation function is a relu activation function; the first discarding layer is used for randomly inactivating half of the neurons, so that overfitting is avoided, and generalization capability is improved; the number of neurons of the second fully-connected layer is half of the number of neurons of the first fully-connected layer, and the activation function is a relu activation function; the second discarding layer is used for randomly inactivating half of the neurons, so that overfitting is avoided, and generalization capability is improved; the number of neurons of the third full junction layer is 2, and the activation function is a softmax activation function, wherein the softmax activation function is used for identifying the probability that the image is a positive example and the probability that the image is a negative example;

the loss calculation submodule is respectively in communication connection with the model training submodule and the sample preprocessing module, and is used for calculating the average loss value loss of the training according to the following formula:

wherein i is 1 to 2ⁿNatural number between, y_iThe diagnostic label code value of the ith standard sample medical image,

the parameter updating submodule is in communication connection with the loss calculating submodule and is used for reversely transmitting the average loss value loss layer by layer through a gradient descent method to update the model parameters;

the iteration control submodule is in communication connection with the loss calculation submodule and is used for circularly and sequentially starting the model training submodule, the loss calculation submodule and the parameter updating submodule until the average loss value loss is reduced to a preset threshold value, and completing model training;

the image acquisition module is used for acquiring medical images to be diagnosed;

the image preprocessing module is in communication connection with the image acquisition module and is used for preprocessing the medical image to be diagnosed to obtain a standard medical image to be diagnosed, which is square, has the size consistent with that of the standard sample medical image and has the normalization mode consistent with that of the standard sample medical image;

the image identification module is respectively in communication connection with the model iteration training module and the image preprocessing module, and is used for inputting the standard medical image to be diagnosed into an AlexNet network model which is trained to obtain a second prediction probability corresponding to the standard medical image to be diagnosed, wherein the second prediction probability comprises the probability that the image is recognized as a positive example and the probability that the image is recognized as a negative example;

the image diagnosis module is in communication connection with the image identification module and is used for obtaining a diagnosis result according to the second prediction probability: and if the probability of identifying the positive example of the image in the second prediction probability is not less than the segmentation threshold of the positive example and the negative example, judging that the diagnosis result is positive example and/or positive, otherwise, judging that the diagnosis result is negative example and/or negative.

7. An apparatus for implementing medical image aided diagnosis based on an AlexNet network model, comprising a memory and a processor, wherein the memory is used for storing a computer program, and the processor is used for executing the computer program to implement the method steps of implementing medical image aided diagnosis based on the AlexNet network model according to any one of claims 1 to 5.

8. A storage medium, characterized in that the storage medium has stored thereon a computer program which, when being executed by a processor, implements the method steps of implementing medical image-assisted diagnosis based on AlexNet network model according to any one of claims 1 to 5.

Technical Field

The invention belongs to the technical field of medical equipment, and particularly relates to a method, a device, equipment and a storage medium for realizing medical image auxiliary diagnosis based on an AlexNet network model.

Background

Medical imaging devices on the market of Medical instruments are available to view Medical images, such as Angiography (Angiography), cardioangiography (Cardiac Angiography), Computed Tomography (CT), Mammography (mammogram), Positron Emission Tomography (PET), Nuclear Magnetic Resonance Imaging (NMRI), and Medical ultrasonography (Medical ultrasonography). However, the current image diagnosis industry has several problems as follows: (1) the culture period of the diagnosis doctor is long, the cost is high, and the gap of the current high-end talents is large; (2) the subjective difference of doctors is very large, and the diagnosis standard is difficult to be completely unified; (3) the doctor only observes by naked eyes, a lot of bottom layer information is invisible, and the information utilization rate is low; (4) too many similar diseases exist, and a great deal of repetitive labor exists in the process of reading the film; (5) the time spent for doctor to read the film is long, at least from tens of minutes to hours.

In view of the above problems, there are two current solutions: firstly, by utilizing a traditional image processing method, aiming at a certain specific disease and a specific type of image film, characteristics are manually extracted, and then judgment is made according to a threshold value, the method needs a large amount of medical and image processing experience and has no universality, and after the part or the image type is changed, the method is difficult to have higher accuracy rate and even cannot work at all; and secondly, the machine learning model or the statistical learning model is utilized, automation is realized to a certain extent, certain universality is achieved, and compared with the first method, the accuracy is greatly improved, but the model still has the problem of insufficient expression capability, cannot cover various complex conditions, and is difficult to perform transfer learning.

Disclosure of Invention

The invention aims to solve the problem that the conventional medical equipment cannot cover various complex conditions when the machine learning model is used for image diagnosis, and provides a method, a device, equipment and a storage medium for realizing medical image auxiliary diagnosis based on an AlexNet network model.

The technical scheme adopted by the invention is as follows:

a method for realizing medical image auxiliary diagnosis based on an AlexNet network model comprises the following steps:

s101, obtaining a plurality of sample medical images and diagnosis labels corresponding to the sample medical images, wherein the diagnosis labels are positive examples or negative examples;

s102, carrying out image preprocessing on each sample medical image to obtain a corresponding standard sample medical image which is square, consistent in size and normalized, and carrying out digital coding on a corresponding diagnosis label in the following form; if positive, it is marked as 1, and if negative, it is marked as 0;

s103, extracting 2 from the training sample data setⁿAnd then, introducing the standard sample medical images into an AlexNet network model for forward propagation to obtain a first prediction probability corresponding to each standard sample medical image, wherein the training sample data set comprises not less than 2ⁿN is a natural number between 4 and 8, and the first prediction probability comprises a probability of identifying the image as a positive example and a probability of identifying the image as a negative example;

the AlexNet network model sequentially comprises a first convolution layer, a first batch of normalization layers, a first maximum pooling layer, a second convolution layer, a second batch of normalization layers, a second maximum pooling layer, a third convolution layer, a fourth convolution layer, a fifth convolution layer, the flattening layer, a first full-connection layer, a first discarding layer, a second full-connection layer, a second discarding layer and a third full-connection layer along the forward propagation direction, wherein the convolution kernel of the first convolution layer is (11, 11), the step length is (4, 4), the number of output channels is 96, the activation function is a relu activation function, and the padding type is valid; the first normalization layer is used for accelerating network convergence, preventing overfitting and improving generalization capability; the pooling core of the first maximum pooling layer is (3, 3), the step length is (2, 2), and the padding type is valid; the convolution kernel of the second convolution layer is (5, 5), the step length is (1, 1), the number of output channels is 256, the activation function is a relu activation function, and the padding type is same; the second batch of normalization layers are also used for accelerating network convergence, preventing overfitting and improving generalization capability; the pooling core of the second maximum pooling layer is (3, 3), the step length is (2, 2), and the padding type is valid; the convolution kernel of the third convolution layer is (3, 3), the step length is (1, 1), the number of output channels is 384, the activation function is a relu activation function, and the padding type is same; the convolution kernel of the fourth convolution layer is (3, 3), the step length is (1, 1), the number of output channels is 256, the activation function is a relu activation function, and the padding type is same; the convolution kernel of the fifth convolution layer is (3, 3), the step length is (1, 1), the number of output channels is 384, the activation function is a relu activation function, and the padding type is same; the flattening layer is used for spreading the characteristic diagram into a line, so that a first full-connection layer is connected to the rear side of the flattening layer conveniently; the number of the neurons of the first full-junction layer is 1024, and the activation function is a relu activation function; the first discarding layer is used for randomly inactivating half of the neurons, so that overfitting is avoided, and generalization capability is improved; the number of neurons of the second fully-connected layer is half of the number of neurons of the first fully-connected layer, and the activation function is a relu activation function; the second discarding layer is used for randomly inactivating half of the neurons, so that overfitting is avoided, and generalization capability is improved; the number of neurons of the third full junction layer is 2, and the activation function is a softmax activation function, wherein the softmax activation function is used for identifying the probability that the image is a positive example and the probability that the image is a negative example;

s104, calculating the average loss value loss of the training according to the following formula:

wherein i is 1 to 2ⁿNatural number between, y_iThe diagnostic label code value of the ith standard sample medical image,

probability of identifying the image as a positive case of the ith standard sample medical image, wherein k is a constant;

s105, reversely propagating the average loss value loss layer by layer through a gradient descent method, and updating model parameters;

s106, returning to execute the steps S103-S105 until the average loss value loss is reduced to a preset threshold value, and finishing model training;

s107, acquiring a medical image to be diagnosed;

s108, image preprocessing is carried out on the medical image to be diagnosed, and a standard medical image to be diagnosed which is square and has the size consistent with that of the standard sample medical image and the normalization mode consistent with that of the standard sample medical image is obtained;

s109, inputting the standard medical image to be diagnosed into an AlexNet network model which is trained to obtain a second prediction probability corresponding to the standard medical image to be diagnosed, wherein the second prediction probability comprises a probability of identifying an image as a positive example and a probability of identifying an image as a negative example;

s110, obtaining a diagnosis result according to the second prediction probability: and if the probability of identifying the positive example of the image in the second prediction probability is not less than the segmentation threshold of the positive example and the negative example, judging that the diagnosis result is positive example and/or positive, otherwise, judging that the diagnosis result is negative example and/or negative.

Optimally, in the step S102, the sample medical image in the square shape is obtained through image preprocessing in the following manner:

when the original shape of the sample medical image is a non-square rectangle, pixel points with pixel values of 0 or pixel mean values are symmetrically filled at two sides in the width direction, so that the final image width is equal to the image length, wherein the pixel mean value is the pixel mean value of all the pixel points in the original medical image.

Further optimally, after the square sample medical image is obtained in step S102, the sample medical images with consistent sizes are obtained through image preprocessing in the following manner:

the sample medical image is changed into the size of 2 by interpolation or pressure sampling^k*2^kWherein k is a natural number between 7 and 12.

In detail, after the sample medical image with the consistent size is obtained in step S102, the normalized sample medical image is obtained through image preprocessing in the following manner:

aiming at each pixel point on the sample medical image, obtaining a normalized pixel value P according to the following formula_New：

In the formula, P_OldThe value is the pixel value before normalization, mu is the pixel average value of all pixel points before normalization, and sigma is the pixel value standard deviation of all pixel points before normalization.

Preferably, before the step S103, the training sample data set is subjected to data enhancement processing in any one or any combination of the following manners (a) to (D):

(A) randomly turning the standard sample medical image up and down and/or left and right, and then adding the obtained image as a new sample into the training sample data set;

(B) carrying out random angular rotation processing on the standard sample medical image, and then adding the obtained image serving as a new sample into the training sample data set;

(C) randomly adding Gaussian noise to the medical image of the standard sample, and then adding the obtained image serving as a new sample to the training sample data set;

(D) and randomly cutting the image of the standard sample medical image, and then adding the obtained image as a new sample into the training sample data set, wherein the image cutting part accounts for no more than 5% of the whole image.

The other technical scheme adopted by the invention is as follows:

a device for realizing medical image auxiliary diagnosis based on an AlexNet network model comprises a sample acquisition module, a sample preprocessing module, a model iterative training module, an image acquisition module, an image preprocessing module, an image recognition module and an image diagnosis module, wherein the model iterative training module comprises a model training submodule, a loss calculation submodule, a parameter updating submodule and an iterative control submodule;

the sample acquisition module is used for acquiring a plurality of sample medical images and diagnosis labels corresponding to the sample medical images, wherein the diagnosis labels are positive examples or negative examples;

the sample preprocessing module is in communication connection with the sample acquisition module and is used for preprocessing images of all sample medical images to obtain corresponding standard sample medical images which are square, consistent in size and normalized, and simultaneously digitally encoding corresponding diagnosis labels in the following forms; if positive, it is marked as 1, and if negative, it is marked as 0;

the model training submodule is in communication connection with the sample preprocessing module and is used for extracting 2 from a training sample data setⁿAnd then, introducing the standard sample medical images into an AlexNet network model for forward propagation to obtain a first prediction probability corresponding to each standard sample medical image, wherein the training sample data set comprises not less than 2ⁿN is a natural number between 4 and 8, and the first prediction probability comprises a probability of identifying the image as a positive example and a probability of identifying the image as a negative example;

the AlexNet network model sequentially comprises a first convolution layer, a first batch of normalization layers, a first maximum pooling layer, a second convolution layer, a second batch of normalization layers, a second maximum pooling layer, a third convolution layer, a fourth convolution layer, a fifth convolution layer, the flattening layer, a first full-connection layer, a first discarding layer, a second full-connection layer, a second discarding layer and a third full-connection layer along the forward propagation direction, wherein the convolution kernel of the first convolution layer is (11, 11), the step length is (4, 4), the number of output channels is 96, the activation function is a relu activation function, and the padding type is valid; the first normalization layer is used for accelerating network convergence, preventing overfitting and improving generalization capability; the pooling core of the first maximum pooling layer is (3, 3), the step length is (2, 2), and the padding type is valid; the convolution kernel of the second convolution layer is (5, 5), the step length is (1, 1), the number of output channels is 256, the activation function is a relu activation function, and the padding type is same; the second batch of normalization layers are also used for accelerating network convergence, preventing overfitting and improving generalization capability; the pooling core of the second maximum pooling layer is (3, 3), the step length is (2, 2), and the padding type is valid; the convolution kernel of the third convolution layer is (3, 3), the step length is (1, 1), the number of output channels is 384, the activation function is a relu activation function, and the padding type is same; the convolution kernel of the fourth convolution layer is (3, 3), the step length is (1, 1), the number of output channels is 256, the activation function is a relu activation function, and the padding type is same; the convolution kernel of the fifth convolution layer is (3, 3), the step length is (1, 1), the number of output channels is 384, the activation function is a relu activation function, and the padding type is same; the flattening layer is used for spreading the characteristic diagram into a line, so that a first full-connection layer is connected to the rear side of the flattening layer conveniently; the number of the neurons of the first full-junction layer is 1024, and the activation function is a relu activation function; the first discarding layer is used for randomly inactivating half of the neurons, so that overfitting is avoided, and generalization capability is improved; the number of neurons of the second fully-connected layer is half of the number of neurons of the first fully-connected layer, and the activation function is a relu activation function; the second discarding layer is used for randomly inactivating half of the neurons, so that overfitting is avoided, and generalization capability is improved; the number of neurons of the third full junction layer is 2, and the activation function is a softmax activation function, wherein the softmax activation function is used for identifying the probability that the image is a positive example and the probability that the image is a negative example;

the loss calculation submodule is respectively in communication connection with the model training submodule and the sample preprocessing module, and is used for calculating the average loss value loss of the training according to the following formula:

wherein i is 1 to 2ⁿNatural number between, y_iIs the ith standard sampleThe diagnostic label code value of the medical image,

probability of identifying the image as a positive case of the ith standard sample medical image, wherein k is a constant;

the parameter updating submodule is in communication connection with the loss calculating submodule and is used for reversely transmitting the average loss value loss layer by layer through a gradient descent method to update the model parameters;

the iteration control submodule is in communication connection with the loss calculation submodule and is used for circularly and sequentially starting the model training submodule, the loss calculation submodule and the parameter updating submodule until the average loss value loss is reduced to a preset threshold value, and completing model training;

the image acquisition module is used for acquiring medical images to be diagnosed;

the image preprocessing module is in communication connection with the image acquisition module and is used for preprocessing the medical image to be diagnosed to obtain a standard medical image to be diagnosed, which is square, has the size consistent with that of the standard sample medical image and has the normalization mode consistent with that of the standard sample medical image;

the image identification module is respectively in communication connection with the model iteration training module and the image preprocessing module, and is used for inputting the standard medical image to be diagnosed into an AlexNet network model which is trained to obtain a second prediction probability corresponding to the standard medical image to be diagnosed, wherein the second prediction probability comprises the probability that the image is recognized as a positive example and the probability that the image is recognized as a negative example;

the image diagnosis module is in communication connection with the image identification module and is used for obtaining a diagnosis result according to the second prediction probability: and if the probability of identifying the positive example of the image in the second prediction probability is not less than the segmentation threshold of the positive example and the negative example, judging that the diagnosis result is positive example and/or positive, otherwise, judging that the diagnosis result is negative example and/or negative.

The other technical scheme adopted by the invention is as follows:

an apparatus for implementing medical image-assisted diagnosis based on AlexNet network model, comprising a memory and a processor, wherein the memory is used for storing a computer program, and the processor is used for executing the computer program to implement the method steps for implementing medical image-assisted diagnosis based on AlexNet network model as described above.

The other technical scheme adopted by the invention is as follows:

a storage medium having stored thereon a computer program which, when being executed by a processor, carries out the method steps of carrying out a medical image-assisted diagnosis based on an AlexNet network model as described above.

The invention has the beneficial effects that:

(1) the invention has created and provided a new method for utilizing the latest deep learning technology in the artificial intelligence to realize the auxiliary diagnosis of medical image, namely in AlexNet network model with very good generalization ability, extract various complicated characteristics with the neural network of convolution first, then utilize the full-connection network to carry on the comprehensive judgement to the characteristic, and then make the model cover various complicated situations, can help in the whole course of auxiliary diagnosis of medical image, realize the automatic diagnosis of end-to-end, efficiency and accuracy are all extremely high;

(2) and newly added parts or images can be migrated and learned, so that the model upgrading becomes simple and easy to operate, the maximum compatibility of the original knowledge can be ensured, and the method is convenient for practical application and popularization.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.

Fig. 1 is a schematic flow chart of a method for implementing medical image-assisted diagnosis based on an AlexNet network model provided by the present invention.

FIG. 2 is an exemplary diagram of a pre-and post-processing image contrast for image fill and uniform size image pre-processing according to the present invention.

Fig. 3 is a schematic diagram of a multi-layer structure of an AlexNet network model provided by the present invention.

Fig. 4 is a schematic structural diagram of a device for implementing medical image auxiliary diagnosis based on an AlexNet network model provided by the invention.

Fig. 5 is a schematic structural diagram of the device for implementing medical image-assisted diagnosis based on the AlexNet network model provided by the invention.

Detailed Description

The invention is further described with reference to the following figures and specific embodiments. It should be noted that the description of the embodiments is provided to help understanding of the present invention, but the present invention is not limited thereto. Specific structural and functional details disclosed herein are merely illustrative of example embodiments of the invention. This invention may, however, be embodied in many alternate forms and should not be construed as limited to the embodiments set forth herein.

It will be understood that, although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of example embodiments of the present invention.

It should be understood that, for the term "and/or" as may appear herein, it is merely an associative relationship that describes an associated object, meaning that three relationships may exist, e.g., a and/or B may mean: a exists alone, B exists alone, and A and B exist at the same time; for the term "/and" as may appear herein, which describes another associative object relationship, it means that two relationships may exist, e.g., a/and B, may mean: a exists independently, and A and B exist independently; in addition, for the character "/" that may appear herein, it generally means that the former and latter associated objects are in an "or" relationship.

It will be understood that when an element is referred to herein as being "connected," "connected," or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may be present. Conversely, if a unit is referred to herein as being "directly connected" or "directly coupled" to another unit, it is intended that no intervening units are present. In addition, other words used to describe the relationship between elements should be interpreted in a similar manner (e.g., "between … …" versus "directly between … …", "adjacent" versus "directly adjacent", etc.).

It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments of the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises," "comprising," "includes" and/or "including," when used herein, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, numbers, steps, operations, elements, components, and/or groups thereof.

It should also be noted that, in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed substantially concurrently, or the figures may sometimes be executed in the reverse order, depending upon the functionality/acts involved.

It should be understood that specific details are provided in the following description to facilitate a thorough understanding of example embodiments. However, it will be understood by those of ordinary skill in the art that the example embodiments may be practiced without these specific details. For example, systems may be shown in block diagrams in order not to obscure the examples in unnecessary detail. In other instances, well-known processes, structures and techniques may be shown without unnecessary detail in order to avoid obscuring example embodiments.